Hyun-Jong Lee, Doosung Hwang

KIPS Transactions on Software and Data Engineering, Vol. 5, No. 10, pp. 483-488, Oct. 2016
10.3745/KTSDE.2016.5.10.483,   PDF Download:
Keywords: Prototype Selection, Nearest-Neighbor Rule, Set Covering Optimization, Greedy Algorithm
Abstract

In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data with hyperspheres, and a hypersphere must cover the data from the same class. The radius of a hypersphere is computed by the mid point of the two distances to the farthest same class point and the nearest other class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that cover all the training data. The proposed prototype selection method is designed by a greedy algorithm and applicable to process a large-scale training set in parallel. The prediction rule is the nearest-neighbor rule and the new training data is the set of prototypes. In experiments, the generalization performance of the proposed method is superior to existing methods.

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